Monostatic synthetic aperture radar (SAR) systems provide a tradeoff between imaging resolution and ground area coverage. In strip-map SAR, the radar antenna is pointed at an area as the platform carrying the antenna moves along a path, collecting data to generate a radar image of a certain resolution and area size.
To meet different application requirements on imaging resolution and area coverage, radar antennas can also be steered, either electronically or mechanically, in different modes. When higher imaging resolution is required, the SAR system operates in spotlight mode by steering the antenna to always illuminate the same spot of interest, generating an image with higher imaging resolution and smaller coverage compared to strip-map mode SAR.
Intermediate SAR modes, such as sliding spotlight SAR (also named as hybrid stripmap/spotlight SAR), are also known. Those modes leverage a trade-off between stripmap and spotlight SAR to generate SAR images with improved azimuth resolution compared to strip mode, and improved ground coverage compared to spotlight.
In the sliding spotlight mode, the radar antenna is steered such that the beam centers intersect at a point farther away from the radar than the area being illuminated. If the intersection point moves closer to an imaging area plain, then the sliding spotlight mode turns to the spotlight mode. If the intersection point moves farther, to infinite, the mode becomes the stripmap mode. In that sense, the sliding spotlight mode is a generalization of both the spotlight mode and the stripmap mode.
When necessary to monitor a larger area, a scan mode SAR is preferred, whose antenna array is steered to scan the area of interest from spot to spot, yielding a larger area but much lower resolution image. However, in many applications, both large coverage and high resolution are desired. This is difficult to achieve in a conventional SAR system with a single baseline observation.
In recent years, the development of compressive sensing (CS) has had significant impact in sensing applications, including radar imaging. CS and its application to radar can be used to ease the trade-off between resolution and coverage. CS theory indicaes that signals may be reconstructed accurately using fewer measurements than previously thought possible. CS-based SAR systems need to measure far fewer reflections, compared to conventional SAR, to image the same area. Prior art work achieves improved resolution, or increased coverage, by randomizing the pulse timing or the beam steering and reconstructing using sparse optimization.
CS enables accurate reconstruction of signals using a smaller number of measurements compared to their Nyquist rate. This sampling rate reduction is achieved using randomized measurements, improved signal models and non-linear reconstruction algorithms. In SAR systems, this translates to significant resolution or coverage improvements. For example, the related applications demonstrate that it is possible to significantly increase an azimuth resolution without compromising the range coverage, and it is also possible to significantly increase the area covered, without compromise in the resolution.